In order to explore dimensionality reduction and classification in hyperspectral remote sensing image

an algorithm based on semi-supervised marginal Fisher analysis(SSMFA) and k-nearest-neighbor simplex(

k

NNS) is proposed in this paper.First

the data are projected from a high-dimensional space onto low-dimensional space by SSMFA combined with the information of different classes.Then

classification is performed under the

k

NNS classifier by using a few neighbors from each class.The experimental results on the Urban data set

Washington DC Mall data set and Indian Pine data set show the effectiven

ess of the proposed algorithm

when i(i=4

6

8) labeled samples and 10 unlabeled samples of each class are randomly selected for training and 100 samples of each class for testing

the overall accuracy of our proposed algorithm is improved by 0.8%-2.5%

2.8%-4.5% and 4.0%-7.0%

respectively

as compared with MFA+

k

NNS

MFA+

k

NN and other methods.